Electronic delivery of information in personalized medicine

ABSTRACT

According to an aspect of an embodiment, a social network system for electronic delivery of information in personalized health care, may include capturing one or more data streams where each of the data streams relates to health care of a patient. The system may further include integrating the data streams to generate integrated diagnostic data and analyzing the integrated diagnostic data to generate analyzed diagnostic data. The system may further include curating the analyzed diagnostic data and generating an integrated report for presentation to a physician of the patient based on the curated analyzed diagnostic data.

CROSS-REFERENCE TO OTHER APPLICATIONS

This patent application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 13/842,004, filed on Mar. 15, 2013, which isincorporated herein by reference.

FIELD

The embodiments discussed herein are related to electronic delivery ofinformation in personalized medicine.

BACKGROUND

Medicine is becoming increasingly personalized, meaning that treatmentsare tailored to a patient's individual health data, including genotypicand phenotypic data. Genotypic data may include selected geneticmarkers, single nucleotide polymorphisms (SNPs), or the entire genesequence. Phenotypic data may include physical exam data from a patient,clinical scores and rating scales, laboratory results such as fromin-vitro tests, and in-vivo imaging data such as magnetic resonanceimaging (MRI) scans. Cost of sequencing is falling rapidly due to noveltechnology such as next-generation sequencing (NGS) and it isforeseeable that such data will become as ubiquitous and low-cost as anMRI scan. Wearable sensors embedded in consumer electronic devices suchas accelerometers and mobile electrocardiogram (ECG) are emerging andprovide means of continuously measuring phenotypic data in real time,via the Internet, giving rise to “digital health.”

Diagnostics is the first step in defining the precise nature of apatient's disease state, typically involving physical measurements thatare transformed into digital information such as a MRI scan into a filein the DICOM image format. Laboratory data can be transformed into aportable document format (PDF) file or delivered in a structured HealthLayer 7 (HL7) format. Patient's disease states can subsequently be“stratified” based on common characteristics, and a tailored treatmentregimen then be chosen that achieves optimized outcomes for the patient.

Alzheimer's disease (AD) diagnosis is complex, particularly in the earlystages of disease (prodromal or pre-symptomatic disease). Diagnosis mayinclude clinical scores (such as cognitive testing) and sophisticatedbiomarkers such as quantitative MM data. Patients with cognitiveproblems are typically first seen by a busy non-specialist primary carephysician (PCP) who may eventually refer the patient to a specialistmemory clinic; however, the early diagnosis of Alzheimer's disease isoften delayed by several years after the first cognitive symptoms. Examsare repeated because they have quality issues and lack standardization,or simply because the specialist did not have access to the previousexams, often because data could not be shared easily. Sometimes a costlyPET scan is ordered by a primary care physician very early in theprocess, without staging the diagnostic process first from low-costscreening to confirmatory diagnostics to increase diagnostic certaintyin a step-wise manner.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one example technology area where some embodiments describedherein may be practiced.

SUMMARY

According to an aspect of an embodiment, a method of deliveringinformation-enabled personalized healthcare in a clinical, non-researchsetting may include capturing one or more data streams where each of thedata streams relates to health care of a patient. The method may furtherinclude integrating the data streams to generate integrated diagnosticdata and analyzing the integrated diagnostic data to generate analyzeddiagnostic data. The method may further include curating the analyzeddiagnostic data and generating an integrated report for presentation toa physician of the patient based on the curated analyzed diagnosticdata.

The object and advantages of the embodiments will be realized andachieved at least by the elements, features, and combinationsparticularly pointed out in the claims. It is to be understood that boththe foregoing general description and the following detailed descriptionare exemplary and explanatory and are not restrictive of the invention,as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 is a diagram illustrating an example social network forpersonalized medicine;

FIG. 2 is a diagram illustrating example data sharing of caseinformation between participants of a social network for personalizedmedicine;

FIG. 3 is a diagram illustrating an example referral data flow betweenparticipants of a private network within a social network forpersonalized medicine;

FIG. 4 is a diagram illustrating example network administration of aprivate network within a social network for personalized medicine;

FIG. 5 is a diagram illustrating an example data flow of adding newinformation to a case within a social network for personalized medicine;

FIG. 6 is a diagram illustrating example diagnostic and therapeutic dataflow within a social network for personalized medicine;

FIG. 7 is a diagram illustrating an example of adding and integration ofcase information within a social network for personalized medicine;

FIG. 8 is a diagram illustrating example components of a system thatuses a social network for personalized medicine;

FIG. 9 is a diagram illustrating an example system for personalizedmedicine;

FIGS. 10-14 are data flows for example methods of generating reports forpresentation; and

FIG. 15 is a flowchart of an example method of deliveringinformation-enabled personalized health care in a clinical, non-researchsetting.

DESCRIPTION OF EMBODIMENTS

Some embodiments herein describe methods and systems for a digitalhealth platform for personalized medicine, particularly in the field ofAlzheimer's disease diagnostics. The disclosure describes theintegration of various data streams, captured from physical measurementsof a patient's disease state, and the electronic routing of suchinformation between primary care physicians and specialists, and a dataanalytics center to facilitate diagnostics and delivery of personalizedtreatments in Alzheimer's disease and other diseases. The systemincorporates a scalable cloud-based social network architecture thatmanages the Health Insurance Portability and Accountability Act (HIPAA)compliant exchange of personal health information, including encryptedfile transfer and messaging, between the participants of the socialnetwork. The exchange of diagnostic information is permission-based andallows referrals to specialists to improve the diagnostic certainty, andis augmented by a data analytics center.

The cloud-based social network architecture allows health care providersto collaborate on a patient case efficiently and share the data in aregulatory compliant manner. Furthermore, the data analytics center ofthe cloud-based social network architecture allows for optimizations inthe diagnostic workflow between health care providers as well as reducesunnecessary exams, improves quality of the data and diagnostic utility,and helps to enable non-specialist physicians to utilize best practices.

Embodiments of the present invention will be explained with reference tothe accompanying drawings.

FIG. 1 is a diagram illustrating an example social network 100 forpersonalized medicine, arranged in accordance with at least someembodiments described herein. The social network illustrates variousparticipants, including an existing user 110, a new user 120, a delegate122, a consultant 130, a lab 140, and a data analytics center 150.Various operations may be performed with respect to the social network100 and the participants within the social network 100. For example, aparticipant may be added to the social network 100, participants in thesocial network 100 may collaborate with respect to medical care of apatient, and data with respect to medical care of a patient may beintegrated and analyzed.

To add a participant to the social network, the participant may first beidentified. For example, the existing user 110, which may be a primarycare physician, may identify the new user 120, which may be a specialtyneurologist, to be added to the social network 100. The existing user110 may have the option to search an external provider database such asthe National Plan and Provider Enumeration System (NPPES) to obtaininitial contact information for the new user 120. The existing user 110may also verify the correct electronic contact information, forinstance, through telephone.

When a participant is being added to the social network 100, theparticipant may designate a participant in a private portion of thesocial network 100 or a non-private portion of the social network 100.In some embodiments, the private portion of the social network 100 maybe for participants that are associated with a hospital network. In someembodiments, the social network 100 may have multiple private portions.As an example of designation of a participant in a private portion ofthe social network 100, the existing user 110 may designate the new user120 to become part of a private portion of the social network 100 whenthe new user 120 is part of a hospital network with which the existinguser 110 is associated. In these and other embodiments, the consultant130 may be an external participant, such as a nuclear medicinespecialist outside of the hospital network, which can perform a positronemission tomography (PET) scan or other diagnostic tests.

The new user 120 may be added to the social network 100 in various ways.For example, in some embodiments, the new user 120 may be added to thesocial network using an invitation. The existing user 110 may enterbasic information into a system used to support the social network 100,such as the system described with respect to FIG. 9, to initiate contactelectronically, such as the new user's 120 e-mail and name. Afterentering the basic information or indicating the basic information forthe new user 120, the existing user 110 may send the new user 120 aninvitation to join the social network 100 for personalized medicine. Insome embodiments, the invitation to the new user 120 may beautomatically generated from a template after the existing user 110indicates the identity of the new user 120.

The invitation may be sent out by e-mail or other messaging system andcontain a link to the login page of the social network 100. An initialpassword for the new user 120 to access the social network 100 may alsobe created automatically. The initial password, for security purposes,may be sent out by a separate message, or delivered by calling the newuser 120 in an office or on a mobile phone of the new user 120. In someembodiments, password authentication may be augmented or replaced bybiometric identity verification such as fingerprint, speech, facerecognition, and/or another electronic access control system. Theanother electronic access control system may include, as examples andwithout limitation, a card-based and a smartphone equipped withelectronic identity verification. In some embodiments, theauthentication method may be a multifactor authentication such as atwo-factor authentication (TFA), which may use the presentation of twoor more of three authentication factors, such as a knowledge factor(such as the password), a possession factor (such as a special accesscard issued by the social network provider), and an inherence factor(such as a biometric factor, e.g., voice or video authentication).

After a user receives an invitation to be part of the social network100, the new user 120 completes the registration process by changing theinitial password to a secure-format password of his/her choice (such ashaving a certain length and characters), and entering additionalinformation, for instance, professional details, address, and othercontact information such as pager, mobile phones, fax number,preferences, etc. into a form.

In some embodiments, the registration process may not require the newuser 120 to enter a password or adding any additional informationregarding the new user 120. For example, authentication may only consistof TFA, as described above. In these and other embodiments, the TFA mayconsist of distributing a special access card for the social network 100or some other access information after verifying the new member'scredentials and obtaining a biometric factor. In some embodiments, thebiometric factor may be obtained by the existing user 110 from the newuser 120, for example at a medical conference. Additional informationabout the new user 120 may also be obtained through other sources, suchas a provider database or credentialing authorities. The additionalinformation may be prepopulated into the form for the new user 120 toease the registration process for the new user 120 while maintainingsecurity and privacy.

In some embodiments, when the new user 120 is part of the privateportion of the social network 100, an administrator associated with theprivate portion of the social network 100 may add the new user 120through an administration module that is associated with the socialnetwork 100. The administrator may be the existing user 110 and/or theadministrator may be another participant in the social network 100 thatis not illustrated in FIG. 1. In these and other embodiments, theadministrator may limit the participants' permissions within the socialnetwork 100 with respect to the ability of the participants to invitenew members, receive, and/or refer/send cases within the social network100.

In some embodiments, when the new user 120 is added to the socialnetwork 100, the new user 120 may be associated with the delegate 122,such as a nurse practitioner or physician assistant that acts on behalfof the new user 120. In these and other embodiments, an administratormay add the delegate 122 through the administration module. In someembodiments, the social network 100 may be searchable for participantswithin the social network 100 for collaboration purposes, such as forinviting participants within the social network 100 to collaborate withrespect to medical care of a patient.

The social network 100 may further be configured to allow participantswithin the social network to communicate regarding a particulartreatment using bidirectional secure messaging communication and/orvideo/voice conferencing within the social network 100. Alternately oradditionally, participants within the social network may communicateusing a tailored mobile communication application. Existingcommunication channels such as text (SMS) messaging, pagers, or e-mailmay further be utilized as well for nonsecure messaging/alerting. Forexample, the nonsecure messaging may be used for indicating to aphysician that his/her attention is required, or to notify participantsof recent activity such as that information has been changed, added, orupdated with respect to a patient.

As mentioned previously, participants may collaborate with respect tomedical care of a patient. Medical care of a patient as used herein maybe referred to herein as a patient case or case. As indicated,participants within the social network 100 may collaborate on a patientcase. This collaboration may take place based on a referral process. Forexample, the existing user 110, which may be a primary care physician(PCP), may refer a patient case to the new user 120, such as aspecialist, after the new user 120 has joined the social network 100 orto the lab 140 for further diagnostic evaluation. The existing user 110may serve as the “patient case owner” during a particular episode ofpatient care and enter basic patient case data, for example, patientdetails such as name, gender, date of birth, contact information, andinsurance information. The existing user 110 may further enterdescriptive information for the case such as priority, type of referral,expected response to referral, and case summary information. Thedescriptive information such as the summary information may further beimported from another health management system, for example, anelectronic medical record (EMR) system. The existing user 110 mayfurther refer a case to multiple members within a private portion of thesocial network 100 or within the entire social network 100 in order tocollaborate on a case. For example, the existing user 110 may refer acase to the consultant 130 during an episode of care (e.g., a phase oftreatment) until the episode of care is considered completed by theexisting user 110.

The collaboration about a case between the participants within thesocial network 100 may include sharing information with the participantsto determine about the case (e.g., patient test result, lab result,diagnostics, patient history, etc.). In some embodiments, the socialnetwork 100 may include a cloud storage for storing information aboutcases. The participants in the social network 100 associated with thecase may be able to access the cloud storage for the case and addinformation to or retrieve information from the cloud storage. Addinginformation to the cloud storage or retrieving information from thecloud storage for a particular case may be referred to herein as addinginformation to the case or retrieving, viewing, or accessing informationfrom the case. Because the case information is part of the socialnetwork 100, the participants within the social network 100 associatedwith the case may access information from the case that otherparticipants have added to the case.

For example, the existing user 110 may add additional case content(along with accompanying metadata/descriptive information) to a case.The additional case content may include the result of a cognitivescreening test, genetic test, and/or a blood test for a disease, such asAlzheimer's disease. The test results may be provided in electronicformat such as a test report in the PDF document format. The report maybe added to the case by file upload or directly from another healthmanagement system such as an electronic medical record (EMR) system.Medical images may be added to the case in a similar manner. Forexample, medical images may be added to the case by the uploading ofDigital Imaging and Communications in Medicine (DICOM) files or directlyfrom a picture archiving and communication system (PACS). Informationfrom tests, such as a cognitive screening test on a mobile device, whichare performed at the patient's home, may be added to the case.Alternately or additionally, information from tests from a PCP orspecialists' offices that have or have not been further augmented by athird party service provider, for instance, a lab service or a dataanalytics center, may be added to the case. In some embodiments, theinformation that may be added to the case in the form of test resultand/or report may contain normative and/or age-related ranges, plots ofthe patient's individual value in relation to the normative and/orage-related ranges, and medical images of the patient or representativeillustrative other cases. The information that may be added to the casein the form of test result and/or report may further contain contextand/or interpretative information such as pointing to a URL or apublication, or including an excerpt or summary of one or manypublications.

The collaboration between participants in the social network 100 may befacilitated by a participant requesting review from another participant.For example, the existing user 110, which may be a PCP, may request thenew user 120, such as a specialist, to join the social network 100 andto be associated with a case in the social network 100. Alternately oradditionally, the existing user 110 may identify that the new user 120is part of the social network 100 using the social network or some othermethod, such as a directory website outside the social network 100. Inthese and other embodiments, the existing user 110 may request that thenew user 120 collaborate on a case with the existing user 110. Therequest may be issued in the manner analogous as that described above orin a different manner. After the new user 120 is set to startcollaborating on the case, the existing user 110 may send the case tothe new user 120 for further evaluation and/or review. In someembodiments, existing user 110 may indicate to the new user 120 thatinformation has been added to the case for the new user 120 to review.As discussed, the indication of new information may be performedmanually, for example, by messaging, or automatically after the newinformation is added to the case.

For example, the existing user 110 may send a patient case to the newuser 120, where the existing user 110 is a PCP and the new user 120 is aconsulting neurologist, for further evaluation. The further evaluationmay include a comprehensive neurological and/or neuropsychological examby means of a computerized cognitive battery. Before sending the patientcase to the new user 120, the existing user 110 may have added casecontents such as screening test results and/or other information such aspatient history and medications, and a case summary for the new user120. The new user 120 may review the case summary information andrelated messages with secure mobile messaging or by a mobile casedashboard application provided by the social network 100 for viewinginformation about a case. The dashboard application may allow previewingof available case contents, for example, reports or already-existingimages such as magnetic resonance imaging (MRI) or PET scans.

In some embodiments, a dashboard application may be navigated through anatural user interface (NUI) driven by speech, touch, gestures, eyetracking, and other input. The dashboard application may be renderedonto a variety of mounted or projected displays and/or flexible orwearable display devices such as eyeglasses capable of displayingcontext-aware superimposed information (augmented reality). In someembodiments, the dashboard application may further be a browser-basedapplication with or without NUI input and allow download of casecontents such as images for further review on third party viewers orapplications such as DICOM image viewer. In these and other embodiments,the browser-based dashboard application may be used within a secureenterprise-computing environment, for example, on a dedicatedworkstation or access devices within a firewall of a hospital, medicalcenter, doctor office, or some other firewall.

When information is added to a case, such information being an image orlaboratory or cognitive test data, the information may have beenobtained without stringent quality standards in place and/or may not bein a form that is comparable between cases or within the same case. As aresult, the information may not be suitable for further analysis, whichmay include quantitative image analysis, next-generation sequencing(NGS) genome analytics, or gene expression analysis. To ensurecomparable data elements within a case and between cases, informationadded to a case may be checked for adherence to quality standards by thedata analytics center 150. For example, data may be checked for certainsource data acquisition parameters and equipment used, prior to furtherprocessing, such as automated analysis of an amyloid PET scan,hippocampal volume quantitation, or DTI fiber tractography using MRIimaging. When information does not meet a certain standard, for example,if information does not allow for suitable quantitation, a message maybe sent back to the participant that sent the information or to otherparticipants connected to the case.

In some embodiments, the information shared on a case within the socialnetwork 100 may not be in a quantitative form. For example, an imageproduced by an MRI or PET scan may not have quantitative informationassociated with the image; however, quantitative information may bederived from the image. In these and other embodiments, image data andother information that is not quantitative in nature may havequantitative image analysis performed to aid diagnosis and to share withother participants in the social network. For example, a radiologist mayhave quantitative analysis performed for longitudinal comparison and/ortreatment decision making. Quantitative image analysis may be performedin addition to a qualitative read of scans such as MRI to exclude otherdisease or aid differential diagnosis, and may be summarized in aqualitative report. Quantitative image analysis may be fully automatedor semiautomated with operator interaction, and be performed onsite/premises on a workstation or server appliance. The quantitationdata or results may then be imported to the case. In some embodiments,quantitative image analysis may be performed on demand at the dataanalytics center 150 separate from where the information is gathered. Insome embodiments, quantitative image analysis may generate a report,such as in PDF format, that contains normative and/or age-related rangesand plots of a patient's individual quantitative imaging values, forexample, hippocampal volumes, in relation to the normative and/orage-related ranges. The report may also include selected images of thepatient. The quantitative imaging report may be interactive and allowviewing of actual medical images in 2-D, 3-D, or 4-D (3-D, over time)and include advanced visualization features such as plots ofquantitative values overlaid with their respective source images, whendata points are selected.

It is to be understood that data in known medical reports and even EMRscurrently is qualitative and text-based (often free text), allowing agreat degree of variability and fuzziness in language. However, it isdesirable to have data in quantitative form or utilize a standardizedvocabulary (ontology) such as Systematized Nomenclature Of MedicineClinical Terms (SNOMED). Mandating a particular data type, however, maylimit utilizing all existing medical information available in a patientcase. To resolve these issues, content curation may be used as anintermediate step for sophisticated capabilities such as integratedreporting, search, semantic integration, and data mining/advancedanalytics. Existing quantitative data may be compiled into conciseintegrated reporting formats presenting one or several biomarkersalongside contextual information such as medical guidelines and/orrelevant excerpts from medical literature or links to the originalreferences. In some embodiments, key original references may be includedin their entireties in the integrated report. Existing nonstandardinformation, such as text data, may be annotated using standardizedvocabularies for subsequent processing such as search, semanticintegration, and data mining purposes. Content curation may utilizefully automated or semiautomated auto-curation software tools anddatabases at the data analytics center 150, or, after anonymization ofdata, third-party on-demand services such as Amazon Mechanical Turk.

In some embodiments, the data analytics center 150 may be configured togenerate an integrated digital diagnostics report after performingquality control, quantitative image analysis, and curation steps. Theintegrated digital diagnostics report may combine one or severalbiomarkers or outcome measures gathered from the lab 140, the new user120, the existing user 110, the delegate 122, the consultant 130, and/orother participants within the social network 100 collaborating on acase. The data analytics center 150 may then curate the sharedinformation into a consolidated view for assessment by a physician, suchas the existing user 110. In some embodiments, the report may beembodied in a mobile application. In some embodiments, the report mayfurther provide longitudinal information on biomarkers or outcomemeasures in the form of plots or other advanced visualizations describedabove. In some embodiments, the existing user 110 or some otherparticipant within the social network 100 that is reading the report mayconsult with an expert physician (proficient in interpreting theintegrated data in the report) in a call center by messaging within thesocial network and as part of a value-added diagnostic service.

In some embodiments, personally-identifiable information, such as names,date of birth, address, and other patient identifying information, maybe stripped from information within the social network 100 during ananonymization process. In some embodiments, third party or open sourcedata anonymization software tools may be employed in the data analyticscenter 150 to provide the anonymization process. An exampleanonymization process may include removing patient identifyinginformation in DICOM image headers. Anonymization may be fully automated(such as when the data is in a standardized format) or semiautomated. Insome embodiments, patient data may be anonymized within the socialnetwork 100. The social network 100 may assign the patient data atemporary or permanent unique identification (UID). The social network100 may allow the physician interacting with the patient associated withthe patient data to associate the patient data with a particular case orpatient. In some embodiments, the temporary or permanent uniqueidentification (UID) assigned to the patient data may be used inconjunction with diagnostic services such as home-based screening. Forexample, a patient may obtain a prepaid card with the UID included in apharmacy. The patient may then visit a PCP that is part of the socialnetwork 100. The PCP may request an anonymized integrated screeningreport from the data analytics center 150, and further consult an expertphysician (proficient in interpreting biomarker combinations/patterns)in a call center, by messaging within the social network 100, and aspart of a value-added diagnostic service. In these embodiments, theexpert physician and the data analytics center 150 may not know theidentity of the patient. Rather the expert physician and the dataanalytics center 150 may only associate the data with the associatedUID. Thus, the identity of the patient may remain confidential withinthe social network 100 even when the case of the patient is worked on byvarious participants in the social network 100.

In some embodiments, the social network 100 may aggregate data collectedin various cases. For example, following quality control, curation, andanonymization steps performed in the data analytics center 150, the datafrom multiple cases may be aggregated in a centralized or federateddatabase, and advanced analytics run against the database. Non-imagingand/or non-sequencing data may be stored in a SQL or NoSQL database(such as Cassandra), while media-rich contents such as imaging or NGSsource data may be stored in file systems for performance reasons. Thedatabase may support semantic data integration to interrelate with datafrom other databases and datasets. The file system storage may bedistributed such as in Hadoop Distributed File System (HDFS). Imagefiles may reside in an external image repository optimized forperformance and referred to by a URL link or other pointer stored in thedatabase.

After data from one or more cases is aggregated into a database, variousadvanced “big data” analytics may be run against the aggregated data.The advanced analytics may include the process of examining largeamounts of data of a variety of types to uncover hidden patterns,unknown correlations, and other useful information. The advancedanalytics may be performed in the data analytics center 150. Forexample, the advanced analytics may include predictive analytics basedon machine learning algorithms and/or data mining or statisticalanalysis techniques (e.g., using R). The advanced analytics may bedistributed (such as in Map Reduce) or parallelized. For example,advanced analytics may predict treatment response, or future onset ofdisease, in a pre-symptomatic Alzheimer's disease patient based on abiomarker pattern and/or genetic profile combination, or calculate aprobability of current disease given a certain combination of factorsthat are included in the case information within the database. Advancedanalytics may further be used to run sophisticated predictive analyticswhich may be based on a PET scan (for instance, fully automated amyloidPET or tau tracer quantification). Alternately or additionally, thesophisticated predictive analytics may be based on a predictive brainnetwork “connectome” analysis based on diffusion tensor imaging (DTI)MRI or functional MRI. The advanced analytics may be performed on aparticular case to assist in determining a diagnosis, treatment, orother aspect related to the particular case within the social network100.

In some embodiments, the advanced analytics may further include use ofsemantic searches against the aggregated data repository to discover andrank cases with known treatment outcome similar to a particular case inorder to provide a personalized treatment in the particular case. Theresults of the advanced analytics may be summarized or otherwisepresented in a report. In some embodiments, the report may be accessedby a participant in the social network 100 through a personalized healthcare (PHC) tailored dashboard that may be driven by a natural userinterface (NUI), as described above.

In some embodiments, the advanced analytics may further be performed bydata scientists. The data scientist may derive new knowledge from theaggregated database that may aid diagnostic certainty and/or providetherapeutic stratification in a particular case.

Modifications, additions, or omissions may be made to the social network100 without departing from the scope of the present disclosure. Forexample, the social network 100 may include other participants thanthose described above. Furthermore, the social network 100 may includevarious other aspects than those described above. For example, otheraspects of the social network may be described with respect to otherFigures herein.

FIG. 2 is a diagram illustrating example data sharing of caseinformation between participants of a social network 200 forpersonalized medicine, arranged in accordance with at least someembodiments described herein. In particular, FIG. 2 illustrates datasharing between a primary care physician (PCP) 210, a specialistneurologist 212, a radiologist 214, and a data analytics center 216. Theparticipants of the social network 200 may further collaborate on sharedcase information 226, using message 218, for example.

FIG. 3 is a diagram illustrating an example referral data flow betweenparticipants of a private network 320 within a social network 300 forpersonalized medicine, arranged in accordance with at least someembodiments described herein. In particular, FIG. 3 illustrates thereferral data flow between participants of the private network 320 thatis part of the social network 300, such as within a hospital thatincludes an existing user 310, a specialist neurologist 312, and an Millfacility 316. FIG. 3 also illustrates an external network 322 that maybe part of the social network 300. The external network 322 may includea PET facility 314 that is configured to share data with theparticipants of the private network 320.

FIG. 4 is a diagram illustrating example network administration of aprivate network 420 within a social network 400 for personalizedmedicine, arranged in accordance with at least some embodimentsdescribed herein. In particular, FIG. 4 illustrates networkadministration of the private network 420, such as within a hospital,through a private network administrator 410 that can add new privatemembers and their delegates, such as a specialist neurologist 412 and adelegate 416. FIG. 4 also illustrates an external network 422 that maybe part of the social network 400. The external network 422 may includea PET facility 414 that is configured to share data with theparticipants of the private network 420.

FIG. 5 is a diagram illustrating an example data flow of adding newinformation 516 to a case within a social network 500 for personalizedmedicine, arranged in accordance with at least some embodimentsdescribed herein. In particular, FIG. 5 illustrates information 516being added by a specialist neurologist 512 that is associated with aPCP 510 and a radiologist 514 in the social network 500. The PCP 510and/or the specialist neurologist 512 may further refer to/consult adata analytics center 518, to generate, for example, an integratedreport concerning health care of the patient. The integrated report maybe added to the case by the data analytics center 518.

FIG. 6 is a diagram illustrating example diagnostic and therapeutic dataflow within a social network 600 for personalized medicine, arranged inaccordance with at least some embodiments described herein. Inparticular, the FIG. 6 illustrates the diagnostic and therapeutic dataflow in the personalized medicine platform for Alzheimer's diseasebetween the participants of the social network 600. For example, FIG. 6illustrates that a PCP 610 may take a cognitive screen test 620 of apatient, which may include ordering an APOE testing and/or a bloodscreening test for Alzheimer's disease that is performed at a Lab 616.The PCP 610 may, after receiving the laboratory reports, consult a dataanalytics center 624, to consolidate the screening data into anintegrated report. The PCP 610 may also refer the patient case to aspecialist neurologist 612 for further evaluation of the patient. Thesestep by the PCP 610 may conclude the screening episode of care for thepatient. The specialist neurologist 612 may order another IVD test fromthe lab 616. The IVD test may entail CSF Abeta/Tau testing. Thespecialist neurologist 612 may further complete a comprehensive exam 618on the patient, that may, in some embodiments, include a fullcomputerized cognitive battery. The specialist neurologist 612 mayfurther refer/order a MRI and/or PET scan 622 from a radiologist 614,which may include image quantition. The specialist neurologist 612 maythen consult the data analytics center 624, to consolidate thecomprehensive evaluation data into an integrated report. These steps bythe specialist neurologist 612 may conclude an comprehensive diagnosticsepisode of care. In some embodiments, the data analytics center 624 maygenerate a predictive report for therapeutic stratification, asmentioned above, that may provide actionable information for thespecialist neurologist 612, or the PCP 610 for prescribing apersonalized drug for the patient related to Alzheimer's disease. Insome embodiments, the PCP 610, may, augmented by the data analyticscenter 624, perform some or all of the functions of comprehensiveevaluation as mentioned above and/or subsequent therapeuticstratification prior to prescribing the personalized drug.

FIG. 7 is a diagram illustrating an example of adding and integration ofcase information within a social network 700 for personalized medicine,arranged in accordance with at least some embodiments described herein.In particular, FIG. 7 illustrates adding 714 the distinct components ofinformation, for example, cognitive scores 720 from a cognitivescreening test, ordered or performed by a PCP 710 to a data analyticscenter 724. In some embodiments, the cognitive scores 720 may further becomplemented by an APOE testing and/or a blood screening test resultswhich may be acquired from a lab 718. Other test results, for instance,imaging data 726 and/or a radiology report 728 may further be added tothe case. Alternately or additionally, the data analytics center 724 mayfurther add integrated reports 716 to the case.

FIG. 8 is a diagram illustrating example components of a system 800 thatuses a social network infrastructure 837 for personalized medicine,arranged in accordance with at least some embodiments described herein.The system 800 may include a social network infrastructure 837 that mayinteract with a local case administration layer 829, an anonymizer, dataaggregation, and analytics layer 838, and a physician presentation layer826. The social network infrastructure 837, in some embodiments, may beconfigured to allow for the interactions between the participants ofsocial networks as described with respect to FIGS. 1-7.

The social network infrastructure 837 may include a scalable,cloud-based case content delivery network 839 that manages aHIPAA-compliant exchange of personal health information, including fullaudit trail, encrypted file transfer, and messaging between participantsof a social network hosted by the social network infrastructure 837. Thesocial network infrastructure 837 may further include system software,such as front-end (client) and server-side application software code,for example, web-enabled, or desktop virtualization application softwarecomponents that may be used to implement the social network's corefunctionality. The social network infrastructure 837 may also includeAPIs for external applications to connect with the social network;application frameworks such as web and rich media applicationframeworks, database server software, and web server software; servervirtualization software, load balancers, networking equipment, andserver and storage equipment. The social network infrastructure 837 mayinclude server equipment that may include diskless server nodes withsolid-state drives (SSD). The social network infrastructure 837 mayinclude storage equipment, which may include flash array storage. Thesocial network infrastructure 837 may also include cloud-networkingequipment such as low-latency network switches, and further includenetwork security and encryption appliances.

The case content delivery cloud 839 may further be deployed in a privatecloud infrastructure such as in a facility owned or rented by the socialnetwork operator, and/or a dedicated cloud facility with appropriatesecurity implemented and managed by a third party. For example, the casecontent delivery cloud 839 may be hosted in a public cloud such asAmazon, or deployed in combination with a private cloud, for instance,in a hybrid cloud architecture with sensitive, non-anonymizedinformation such as protected health information. In some embodiments,the case content delivery cloud 839 may further be deployed in a PaaS(platform as a service) environment, such as Force.com, that mayeliminate complexities of managing the software and hardwareinfrastructure layers and automatically scaling the infrastructure asdemand grows.

In some embodiments, the case content delivery cloud 839 may further beconnected to specialized (such as for data transfer speed and certainviewers) third-party cloud-based repositories, for instance, image orgenome sequencing data repositories, through the use of an API. In theseand other embodiments, pointers such as URLs or XDS may be used to linkcontent stored in the case content delivery cloud 839 to correspondingcase content in the third-party repository.

The social network infrastructure 837 may be configured to communicatewith the local case administration layer 829. The local caseadministration layer 829 may be configured to allow for local caseadministration by back-office personnel such as physician's assistants,nurses, or technicians may reside within the enterprise or health careprovider's (such as hospital or practice) firewalls 860. Patient caseswithin the social network infrastructure 837, which may be cloud-based,may be accessed and managed through a device 834, such as a hospitaldesktop, laptop computer or mobile device via a web browser interface832 (such as Firefox, Internet Explorer, Chrome, or Safari), or anon-browser based native application installed on the device 834, forexample, an iOS, Android, Windows, or Mac OS software application. Casecontent, such as images or reports or other content, may be uploaded viaa file uploader module 830 accessed by the case composer module 831. Thecase composer module 831 may provide functionality to enter a newpatient, find an existing patient, or specify recipients for the casesuch as a specialist neurologist and/or data analytics center within thesocial network infrastructure 837. Case composer module 831 may furtherallow metadata, for instance, content describing information (such ascategory, description of files uploaded) to be added. Metadata may beimported directly from an EMR such as hospital EMR or cloud-based EMR840. Case content may be organized in folders or directories. Actualcase content may further be uploaded directly, such as images from alocal DICOM server 846 (node) or cloud-based remote image repository.Similarly, case content can be downloaded onto a secured local device834 such as for image viewing within a third party image viewer 836. Anetwork administration module, as described earlier, may further allowgenerating a full HIPAA-compliant (and time-stamped) audit trail on allpatient data within the social network infrastructure 837, for example,as to which user accessed certain patients.

In some embodiments, the local case administration layer 829 may includea locally installed cache server 835 that may be configured to replicateand/or prefetch content from the social network infrastructure 837 forfaster access such as the third party image viewer 836. In someembodiments, an SSL connection may be established between cache server835 and case content delivery cloud 839 to allow the cache server 835 toaccess information from the social network infrastructure 837.Similarly, content such as images may be batch uploaded using the cacheserver 835. The cache server 835 may be a software application locallyinstalled on a local computer within the firewalls 860 of the hospital,or a network appliance, such as residing in a data center. The cacheserver 835 may be an embedded system appliance with flash memory,integrated firewall, and wireless networking capabilities. Alternatelyor additionally, the cache server 835 may be directly connected withphysician interfacing applications and devices 834 such as tablets andwearable computing devices. Wearable computing devices may include, forexample, eyeglasses with display capabilities.

In some embodiments, the cache server 835 may further be configured toencrypt or decrypt data stored on the cache server 835 prior to sendingdata to, or after receiving encrypted data from, the case contentdelivery cloud 839. In these and other embodiments, just encrypted datais stored in the case content delivery cloud 839 and could, forinstance, be hosted in a public cloud.

In some embodiments, the local case administration layer 829 may beconfigured to communicate with the enterprise plugin layer 850. Inparticular, the case composer module 831 may be configured tocommunicate with or include plugins 841 that may be configured toprovide interoperability between the local case administration layer 829and other local health management systems within the health careprovider's firewall 860. The other local health management systems mayinclude EMRs 840, PACS 846, or other systems 842 supporting HL7messaging protocols, such as laboratory information management systems(LIMS). The plugins 841 may allow import (or export) of data from/tosuch systems to the local case administration layer 829.

For example, the case composer module 831 may invoke a pluginapplication to connect with local EMR 840 to import case metadata for agiven case. As another example, medical images may be imported into acase through the case composer module 831 after they are received from alocal or cloud-based PACS 846. As another example, laboratory resultsmay be imported into a case through the case composer module 831 afterthey are received from a LIMS via HL7 v2.x messaging. In someembodiments, the case composer module 831 may use the plugins 841 toaccess information from a personal health record (PHR) such asHealthVault. For example, the case composer module 831 may also use theplugins 841 to import via CCR (continuity of care) or Direct protocol848 patient demographics, insurance information, medications, allergies,and care plan, among other information. In some embodiments, the plugins841 may be pre-installed in the cache server 835.

In some embodiments, the plugins 841 may be configured to provideinformation to the case composer module 831 from mobile data capturedevices 844. The mobile data capture devices 844 may include devicesused for cognitive screen testing 620. In these and other embodiments,the case composer module 831 may connect directly with the mobile datacapture devices 844 to a server device that stores the captured data,such as from a home-based screening device connected via the Internet.Similarly, a server device 845 may be used to run quantitative analysison images, or on gene sequencing data, and export analysis data into thecase using the case composer module 831. The server device 845 mayfurther be a specialized or compact supercomputer for medical imagesanalysis or NGS genome analysis, a NGS genome sequencing desktop device,or portable USB sequencing device. The server device 845 may also allowautomatic backup of data within the social network interface 837 and/orfunction as a cache server 835, as described above.

The local case administration layer 829 and the social networkinfrastructure 837 may be further configured to communicate with aphysician presentation layer 826. Physicians typically aretime-constrained and require a different type of presentation layer thanback-office personnel such as technicians or physician's assistants. Tooptimize delivery of information to a busy physician, various systemsand applications may be utilized. For example, a mobile phone messagingapp 820 or integrated reports 822 delivered through applications onmobile devices such as tablets or “phablets” may be used. Alternately oradditionally, wearable display devices, such as intelligentwristbands/watches that may connect wirelessly with a patient'swristband/watch (for instance, with built-in cognitive self-test orcontinuous monitoring application), and invoke presentation of apatient's integrated report within the intelligent wristband/watches. Inthis manner, a physician may receive information about a patient whenthe patient arrives to see the physician.

In some embodiments, the physician presentation layer 826 may include aninteractive dashboard 824 tailored for personalized health care (PHC)that may use touch, voice, or other natural user interface (NUI) asinputs. The interactive dashboards 824 may be displayed on flexible ordisposable display devices folded into drug packaging materials.Alternately or additionally, the interactive dashboards 824 may bedisplayed on wall-mounted TVs that may wirelessly connect with apatient's wearable computer wristband/watch and thereby invokepresentation of the patient's information to the physician. Interactivedashboards 824 may further be projected by a wirelessly-connectedprojector device against the walls of a treatment room or doctor'ssuite.

Image viewing capabilities, such as from a third part image viewer 836,such as a DICOM viewer, may be integrated into dashboards, integratedreports, or run as browser-based or standalone applications. In theseand other embodiments, images may be downloaded onto a device, such asthe device 834, for image review purposes or imported into a local orremote PACS system 846. In some embodiments, image viewing functionalitymay not require that the actual images be downloaded onto a device. Inthese and other embodiments, application components may transmit pixeldata that may be rendered onto the device display, while the actualimage files reside on a local server, such as the cache server 835 or ona remote server, such as a server within the social networkinfrastructure 837. In some embodiments, to render images withoutdownloading the images, the physician presentation layer 826 may utilizethe HTML5 web standard, Java, or desktop virtualization technologies.

The physician presentation layer 826, the social network infrastructure837, and the local case administration layer 829 may be configured tocommunicate with an anonymizer, data aggregation, and analytics layer838. The anonymizer, data aggregation, and analytics layer 838 mayinclude one or more sub-systems that provide functionality tode-identify patient data, and store such de-identified data in acentralized or federated repository for further analysis, such asadvanced “big data” analytics. For example, the anonymizer, dataaggregation, and analytics layer 838 may include an anonymizersub-system, a data aggregation sub-system, and/or an analyticssub-system.

In some embodiments, the anonymizer sub-system that performs thede-identifying of patient data may be a server component installedinside the local case administration layer 829. The data aggregationsub-system may consist of a large-scale and/or distributed database andfile storage system, installed on premises, or in an external facility,for instance, at a cloud services provider such as Amazon.

The analytics sub-system may consist of a server-based system, such as acompute node or plurality thereof, that are configured to runapplication software for automated quantitation of images, such ashippocampal volume measurements. The analytics sub-system may further becloud-based, such as a private cloud-based compute resource andconnected to the social network infrastructure 837 or other third partyinfrastructures discussed herein. The analytics sub-system may furtherconsist of an advanced, automated analytics system and, as describedearlier, such as installed in a data center, cloud-computing resourcesuch as Amazon EC2, or a “data center in the cloud.” The advancedautomated analytics subsystem may run analytics against the aggregateddata and be in close proximity thereof, for instance, in the samefacility.

The system 800 illustrated in FIG. 8 may be used for the electronicdelivery of information in personalized medicine and Alzheimer's disease(AD) diagnostics. The system 800 may perform the electronic delivery ofinformation by utilizing a scalable cloud-based social networkarchitecture to capture multiple patient data streams and then routesuch information between various health care providers. The system alsoincludes a data analytics center (such as a fully automated “big dataanalytics” system component) that analyzes the data and presents theinformation in distilled and “curated” form to a prescriber (forinstance, on mobile devices). The system 800 is applicable to anypersonalized health care application and therapy area, for example,neurodegenerative diseases, multiple sclerosis, and cancer, amongothers.

The system 800 is, without limitation, particularly well-suited in thecontext of biologics drug therapies that warrant personalization forrisk/benefit and economic considerations. The system 800 is furtherwell-suited to connect with mobile/wireless sensors that acquire realtime and continuous data streams. The system 800 is further well-suitedfor using imaging and/or next-generation sequencing data which mayrequire data analytics prior to using the imaging and/or next-generationsequencing data for personalizing health care in a real-world clinicalapplication context outside academic research. The system 800 mayfurther be implemented in computer software, or in hardware circuitry,or any combination of software and hardware components, and is notlimited to any specific software or hardware implementation.

FIG. 8 illustrates some of the major components of a system describedherein and the data flow for providing personalized medicine. In someembodiments, a service provider/operator may implement components 837,839, 838, 820, 822, 824, 830, 831, 835, 831, 841, 844, and 845, of thesystem 800, and provision services and devices to certain users and/ormembers of the social network infrastructure 837 on an on-demand,subscription, and/or pay-per-use basis, or other monetization basis suchas fee-based, advertising supported, or on “freemium” basis. The membersmay include physicians, back-office personnel, such as physicianassistants or nurses, and may control and own certain devices andsoftware 826, 834, 832, and 860 to access the implemented components837, 839, 838, 820, 822, 824, 830, 836, 835, 831, 841, 844, 845, forinstance, and to securely upload protected health information fromhospital or external repositories 840, 842, 846, and 848.

In some embodiments, the case content delivery cloud 839 component ofsocial network infrastructure 837 may be implemented as a web-basedsystem using a number of common web technologies such as AJAX, LAMPstack, Java, Javascript, XML/XSLT, Python, web application frameworkssuch as Ruby, ASP.NET, and/or other proprietary frameworks, serverlibraries, and GUI components that enable rapid development of custom,AJAX-enabled cross-browser applications. Web services may further beimplemented via RESTful APIs, which may include open-source SMART API,to drive interactive reports 822 and interactive dashboards 824; or APIsto drive third-party natural interface (NUI) devices, for example,augmented-reality eyeglasses. In some embodiments, native applicationsmay be installed within the physician presentation layer 826, or localcase administration layer 829 (such as on the device 834), that connectsecurely via the Internet to a back-end component of the case contentdelivery cloud 839 system. Such security may include security via256-bit, 512-bit, 1024-bit AES SSL, or higher bit strength encryptionsthat may be implemented for secure Internet communications, or byimplementing other protocols, for example, transport layer protocol(TSL).

In some embodiments, native mobile applications within the physicianpresentation layer 826 may, for instance, be developed using mobiledevelopment tools such as Android or iOS SDK, Corona SDK, Sencha, orUnity, among others. Other development tools may further be used toimplement certain native interactive charting/reporting functionalityfor integrated reports 822 and interactive dashboards 824, as describedearlier, and third party SDKs for wearable computing devices. Otherdevelopment tools may further be used to implement other types of NUIdevices such as gesture-based controllers, for example, Kinect, orLeapMotion, among others.

Access control to the social network infrastructure 837 and/orapplications within the physician presentation layer 826 may, forexample, be implemented using a web-based login page (with encryptedpassword submission), single sign-on (SSO) approaches (such as LDAP,Active Directory, OpenSSO), or biometric SSO. Biometric identityverification may further include one or more biometric factors, forexample, gesture, hand shape, EEG, eye tracking, retina signatures,fingerprint, speech, face recognition, and/or other biometric featurescaptured from an access device. Access control may further beimplemented using certain SDKs/APIs or other electronic access controlapproaches, for example, a card-based approach or a smartphone equippedwith electronic identity verification.

In some embodiments, the case composer module 831 may be embodied in aweb-based application, such as Javascript. In some embodiments, the fileuploader module 830 may be implemented in Java or Flash. In someembodiments, the front-end (client) applications on devices in the localcase administration layer 829 (such as on the device 834), or on adesktop computer in the physician presentation layer 826 may beimplemented using integrated development environments (IDEs) such asVisual Studio, Xcode, Eclipse, and other software developmentenvironments. DICOM viewing may further be integrated using OsiriX.

The various plugins 841 for bidirectional (import/export)interoperability with other systems, for instance, hospital, external,or locally-installed instruments/appliances or other embodiments withinthe enterprise plugin layer 850 may be embodied as a native desktop orserver application or Java application. Alternately or additionally, thevarious plugins 841 may be implemented using an IDE, such as the IDEpreviously mentioned. Open source interface engines, for instance, MirthConnect and/or SMART API, may further be used to implement said pluginsto hospital or external repositories such as HL7, CDA, DICOM, and/orfrom PHRs via CCR/Direct protocol. The various plugins 841 may furtherbe embedded pre-installed in the above-mentioned instruments such as NGSsequencing devices, specialized devices supporting local-quantitativemedical images analysis, and backup to the social network infrastructure837. The various plugins 841 may further be embedded pre-installed inthe cache server 835.

In some embodiments, the cache server 835 may be implemented as asoftware application and installed on a local computer within thehospital's firewalls or on a server residing in a data center. The cacheserver 835 may further be implemented in embedded system code, forinstance, running under Embedded Linux distributions (for example,OpenWrt) or real-time operating system (RTOS) such as VxWorks orNeutrino. In some embodiments, the cache server application code may beimplemented using IDEs such as Eclipse, Tornado, QNX, Visual Studio,Xcode, and other software development tools. The cache server 835 mayfurther encrypt or decrypt data stored on the cache server 835, prior tosending data to, or after receiving encrypted data from case contentdelivery cloud 839. In these and other embodiments, encrypted data isstored in the case content delivery cloud 839, and could, for instance,be hosted in a public cloud. Encryption may, for instance, beimplemented via FUSE-based EncFS encrypted file system in Linux orTrueCrypt for other operating systems.

The cache server 835 may, in some embodiments, have a very compact formfactor and designed for small-size physician practices. For example, thecache server 835 may include a turnkey-embedded system appliance withflash memory and wireless networking capabilities, for instanceBluetooth, NFC, or Wi-Fi. In these and other embodiments, the cacheserver 835 may further incorporate integrated firewall and networkintrusion detection capabilities.

In some embodiments, the cache server 835 may wirelessly connect withphysician-interfacing applications and devices within the physicianpresentation layer 826 such as tablets and wearable computing devices,for example, smart eyeglasses with built-in displays and/or havecircuitry components to drive other types of natural user interfaces,such as gesture-controlled interfaces. The cache server 835 may beimplemented using an RTOS, such as VxWorks, or a secure embedded Linuxdistribution to ensure high stability and security.

The anonymizer, data aggregation, and analytics layer 838 may beembodied in an analytic data center and comprised of sub-systems thatprovide functionality, as described earlier, to de-identify patient dataand store such de-identified data in a centralized or federatedrepository for further analysis. The system 800 may include one ormultiple of the analytic data centers. In some embodiments, the analyticdata center may be tailored to the needs of certain customers, such asproprietary reference data from a pharmaceutical company, health system,payor, or public-private partnership. The tailored analytic data centermay be run, for example, in a private cloud setting.

An anonymizer sub-system within the analytic data center may be a servercomponent installed inside the firewall of the analytic data center.Anonymizer may be implemented with third party or open source dataanonymization software tools, such as XNAT for DICOM data, Mirth Connectfor HL7 data, and/or custom Python shell scripts may be written to stripoff protected health information such as patient identifyinginformation.

In some embodiments, data formats of data in the analytic data centermay be standardized before further analysis, for instance, toquantitative information, in favor of qualitative data. In these andother embodiments, curation of the data may be fully automated for thestandardized data types. In some embodiments, the quality control andanonymization of data imported into case content delivery cloud 839 mayfurther be embodied in one or more plugins of the various plugins 841,such as mobile data capture devices 844 and server device 845 plugins.For example, these plugins may be developed with open source softwarecomponents such as RSNA CTP. The plugins with quality-control (such asfor correct image acquisition parameters in a DICOM header) andanonymization functionality, may further be complemented by Python shellscripts implemented on a server in the analytic data center and maycheck image data for defects and reject defective images prior tostoring/aggregating such data.

Non-imaging and/or non-sequencing data may be stored in a SQL (such asMySQL) or NoSQL database (such as Cassandra, MongoDB, or Hbase), and/orHive, while media-rich contents such as imaging or NGS source data maybe stored in file systems, for example, distributed HDFS for performancereasons. The case content delivery cloud 839 may support semantic dataintegration to interrelate with data from other databases and datasets,for instance, ADNI or ConnectomeDB. Image files may further reside in anexternal cloud image repository optimized for performance and referredto by a URL link or other pointer stored in the case content deliverycloud 839, or via APIs such as RESTful APIs.

The analytics sub-system within the analytic data center may be embodiedin a server-based system inside the firewall of the analytic datacenter, such as a compute node or plurality thereof. The analyticssub-system may be configured to run application software for automatedquantitation of images, for example, hippocampal volume measurementsand/or quantitation of other brain structures using MRI, voxel-basedamyloid PET quantitation, texture analysis of MRI scans, or brain“connectome” analysis based on DTI fiber tracking, and fMRI. Automatedquantitation may, for example, be implemented in MATLAB computer codeand compiled as executable or C/C++ shared library, as part of anautomatic quantitation server code. In some embodiments, the server codefor the analytics sub-system may further be parallelized. In someembodiments, automatic quantitation code within the analytics sub-systemmay further exhibit certain numerical instabilities of calculations,which may be caused by the high temperature of a computer chip, cosmicradiation, moisture, and/or manufacturing defects. The automatedquantitation code may further correct for such numerical instabilitiesby incorporating external real-time data, such as sensor data in thecalculation, and/or including re-calculations on another computer chipto flag calculation errors based on the chip manufacturing defects. Suchcalculation errors may further be detected by running random checkcalculations on external reference datasets, for example, imaging datain the ADNI or other databases, and comparing the random checkcalculations from external reference datasets against manually-tracedvolumes in such database. The automated quantitation code may furtherstore a physical hardware signature of the compute node on which code isrunning on. For example, dmidecode in Linux may be used to obtaindetailed information of the chip used in performing the calculation;said hardware signature may then be compared against external hardwarereference data, to flag potential calculation errors due to hardwareused in certain circumstances, and, given said external sensor data, maythen allow correction by performing a recalculation at a later time/ondifferent hardware.

In some embodiments, the analytics sub-system may be configured toperform connectome analysis, which may be based on data from ultra-highresolution DTI MRI or resting-state fMRI.

In some embodiments, the analytics sub-system may further includeso-called “social network analysis” (SNA) tools that may be implementedusing open source tools such as Cytoscape and R tools for SNA. The SNAtools, in one particular embodiment, may be utilized to perform brainconnectome analysis. In another embodiment, SNA tools may be used toperform analysis on the social network infrastructure 837. For example,the SNA tools may perform analysis on the social network infrastructure837 to discover certain patterns of user interaction between theparticipants of the network and improve services offered to theparticipants. Alternately or additionally, the SNA tools may performanalysis on the social network infrastructure 837 to provide insights topayors to optimize delivery of health care in a cost-effective way. TheSNA tools for brain connectome analysis may, in yet another embodiment,incorporate other graph theory analysis tools, for instance, BrainConnectivity Toolbox (BCT) or MatlabBGL. The Brain Connectivity Toolbox(BCT) may, for example, be used to calculate “small-world network”indexes and properties of patients suspected of having Alzheimer'sdisease. The Brain Connectivity Toolbox (BCT), in another embodiment,may further be used for visualization to aid data scientists orphysicians using interfaces in the physician presentation layer 826,such as Brainnet Viewer, Connectome Viewer Toolkit, or topological graphtheory visualization tools.

In some embodiments, the analytics sub-system may, in anotherembodiment, run application software to perform automated “big data”analytics, as described earlier. The automated analytics may runanalytics for data of a patient against aggregated data of multiplepatients, for example a large number of patients. The analyticssub-system may be embodied in an analytic data center, cloud-computingresource such as Amazon EC2, Google Compute Engine, Azure, or ananalytic data center in the social network infrastructure 837.

In some embodiments, the analytics subsystem may further be in closeproximity to the aggregated data, for instance, in the same facility. Insome embodiments, the analytics subsystem may provide in-memoryanalytics with the analyzed large-scale (e.g., terabyte-order or higher)data residing in memory. The in-memory analytics may reduce latency andprovide instant analytics. In some embodiments, the analytics subsystemmay further be accessed by physician applications and devices 826 and836, as described earlier. In these and other embodiments, a firstanalytics server, for instance, an in-memory analytics server, may beused to render certain interface components to or physicians in thephysician presentation layer 826, or to data scientists. A second webserver may be used for interface components that may be rendered.Alternatively or additionally, desktop virtualization techniques may beused for rendering interface components (for instance, Citrix, or opensource alternatives).

In some embodiments, the analytics system may implement machine learningor other data mining techniques, for example, natural languageprocessing, neural networks, Support Vector Machines (SVM), andstatistical classifiers such as k-Nearest Neighbor (k-NN), or LinearDiscriminant Analysis (LDA). The data mining techniques may beimplemented using, for example, NLTK, R, or MATLAB, or by using theApache Mahout machine-learning library, which is built on top of theHadoop system and may be highly scalable.

In some embodiments, a physician may request analytics to predicttreatment response, future onset of disease, or calculate a probabilityof current disease given a certain combination, among other things, in apre-symptomatic Alzheimer's disease (AD) patient. The analytics in theseand other embodiments may be based on a biomarker pattern combination ofthe patient. The machine learning algorithms, for instance, a supervisedneural network or SVM classifier, may be trained on aggregated data frommultiple other patients. The trained algorithm may then be applied tothe patient's biomarker combination. The trained algorithm may furtherbe trained to estimate Bayesian a posteriori probabilities, to estimatethe probability of disease with a given set of biomarkers based on knownconversion to AD status from aggregated longitudinal data.

A physician, through the physician presentation layer 826, may alsoinvoke further analytics. The further analytics may include theidentification of useful add-on markers by calculating predictive powerof the add-on markers, for example, by calculating area under thereceiver operating characteristic (ROC) curve or AUC values of theproposed biomarker combination from the aggregated data. Theidentification of useful add-on markers may be performed by running asimulation of adding default (e.g., mean) values for the new biomarkers.Based on the simulations, the analytics system may calculate a revisedBayesian a-posteriori probabilities, with the add-on markers in order toestimate the potential gain in diagnostic certainty. Certain statisticalclassifiers may further use a priori (prior) probability of disease asmodel input, which could, in another embodiment, be estimated based onrisk factors and epidemiological data, such as cardiovascular riskfactors and age, when these variables are not included directly as inputvariables.

FIG. 9 is a diagram illustrating an example system 900 for personalizedmedicine, arranged in accordance with at least some embodimentsdescribed herein. The system 900 may include social network 910, a datasystem 920, and a natural user interface (NUI) unit 940 that may becommunicatively coupled and allowed to exchange informationtherebetween.

The social network 910 may be configured analogous to the social network100 of FIG. 1. Alternately or additionally, the social network 910 maybe analogous to the social network infrastructure 837 of FIG. 8. In someembodiments, the social network 910 may be configured to facilitateinteractions between multiple participants with respect to health carediagnostics of a patient. In some embodiments, the data system 920 maybe included in the social network 910 or outside of the social network910. The social network 910 may store patient data, such as biomarkersfrom images and other tests performed on or by one or more patients. Insome embodiments, the patient data may be imported via the natural userinterface unit 940 or some other interface.

The natural user interface unit 940 may be analogous to the physicianpresentation layer 826 of FIG. 8, and may be configured to send data toand receive data from the social network 910 and the data system 920.Additionally, the natural user interface unit 940 may be configured tointeract with a physician associated with the patient. The natural userinterface unit 940 may receive instructions from and present informationto the physician. In some embodiments, the natural user interface may befurther configured to instruct the data system to analyze data based onan instruction from the physician.

The data system 920 may include various units, including a dataanonymizer unit 922, a data aggregation unit 924, a data analytics unit926, and a reporting unit 930. In some embodiments, the data system 920may be analogous to analytic data centers discussed herein.

The data anonymizer unit 922 may be configured to receive patient dataassociated with the health care diagnostics of a patient and to removeat least a portion of patient identifying information from the receivedpatient data to generate first anonymized data. The data anonymizer unit922 may be analogous to the anonymizer sub-system discussed with respectto FIG. 8. In some embodiments, the patient data being sent to thesocial network 910 via the natural user interface unit 940 may bereceived by the data anonymizer unit 922 first and a portion of thepatient identifying information may be removed. In some embodiments,patient data in the social network 910 may be sent to the dataanonymizer unit 922 and the patient identifying information may beremoved from the patient data. The patient data may then be sent back tothe social network 910.

The data aggregation unit 924 may be configured to store the anonymizedpatient data and to store anonymized data of at least one other patient.

The data analytics unit 926 may be configured to analyze the patientdata using other patient data from within the social network 910, inparticular from data aggregation unit 924. For example, in someembodiments, the patient data may include biomarkers. By analyzing thebiomarkers of the patient data in relation to biomarkers from data fromother patients or healthy individuals, information concerning thepatient may be determined. The information may relate to diagnosis of adisease, therapy of a disease, and/or progression of a diagnoseddisease, among other things. In some embodiments, the data analyticsunit 926 may be configured to curate the patient data and/or informationdetermined by the data analytics unit 926. The data anonymizer unit 922may be analogous to the analytics sub-system discussed with respect toFIG. 8.

The reporting unit 930 may be configured to receive information and thepatient anonymized data in order to generate a report that may bepresented to the physician through the natural user interface unit 940.

Modifications, additions, or omissions may be made to the system 900without departing from the scope of the present disclosure. For example,the system 900 may include other modules, units, or systems than thosedescribed above. Furthermore, the social network 910 may include variousother aspects than those described above.

FIG. 10 is an example data flow of an example method 1000 of generatinga report for presentation, arranged in accordance with at least someembodiments described herein. For example, in the method 1000, anintegrated screening report may be generated from a low-cost screen forearly Alzheimer's disease (AD), such as prodromal AD. The report mayconsist of a patient completing the low-cost screen, such as a cognitivetest, on a mobile device at the patient's home or at a primary carephysician's office. In some embodiments, the cognitive test may beadministered using a web-based application, such as an HTML5 webapplication or using a native app on a mobile device or some othercomputing device.

After completing the cognitive test, when the patient receives a scoreabove a threshold that indicates a likelihood of AD, additional testsmay be ordered and/or taken by the patient. For example, the patient mayundergo a genetic test for an APOE genotype and/or testing for certaingenetic variations, such as single-nucleotide polymorphisms (SNPs).Alternately or additionally, the patient may undergo in-vitrodiagnostics (IVD) screening tests such as a blood test or an eye-basedscreening test configured for detecting the presence of ocular depositsof amyloid beta peptide. The patient may undergo other tests as well,such as emerging screening tests that may include continuous wirelessmonitoring by gait sensors, eye tracking, or wireless sleep monitorssuch as accelerometers and/or electroencephalogram (EEG) sensors, etc.

In some embodiments, the patient or primary care physician of thepatient may order the additional tests. In some embodiments, theadditional tests may be ordered directly through the cognitive testingapplication. For instance, the patient may press an order button on theweb-based application or receive a coupon from an in-app purchase. Insome embodiments, the additional tests may be performed by a personalgenome service's online store, such as 23 and Me. Alternately, thepatient may already have an account with the personal genome service andlog into the account to retrieve the patient's genetic data through anapplication-programming interface and import the data into a socialnetwork cloud, such as the case content delivery cloud 839 of FIG. 8.

The data from the additional test may be captured in a social networkcloud and combined with the data from the cognitive test. The combineddata from the patient may be analyzed and curated in a concise,summarized form, as described above. For example, the combined data maybe curated into a report, such as an integrated screening report. Theintegrated screening report may suggest a comprehensive diagnosticevaluation. Alternately or additionally, the integrated screening reportmay not suggest a comprehensive diagnostic evaluation. Whether theintegrated screening report suggests a comprehensive diagnosticevaluation may be based on whether the combined data suggest thelikelihood of AD being above a threshold. In some embodiments, thecombined data may be compared or analyzed with respect to other data ofother patients that may or may not have been diagnosed with AD. Thisanalysis may assist in determining when the combined data may indicate alikelihood of AD.

When the report indicates a likelihood of AD or otherwise indicates thata comprehensive diagnostic evaluation should be performed, a primarycare physician of the patient may send to/share with a specialist in thesocial network.

FIG. 11 is an example data flow of another example method 1100 ofgenerating a report for presentation, arranged in accordance with atleast some embodiments described herein. In some embodiments, the method1100 may begin with a specialist having received a report indicatingthat a screening diagnostic evaluation be performed based onpreviously-collected data from a primary care physician. In someembodiments, the report received by the specialist may be analogous tothe report received by the specialist in the method 1000.

In particular, the method 1100 is configured to generate an integrated“baseline” diagnostics report. The baseline diagnostics report may be areport that includes baseline data for a patient. The baseline data maybe data that is gathered from a patient and used to compare to futuredata of the patient to determine the health of the patient. The baselinedata determines baseline health characteristics of a patient.

The specialist may order additional tests for the patient, such as anMRI and/or a spinal tap and cerebrospinal fluid (CSF) assay for tauprotein or amyloid beta peptide, which may be diagnostic markers ofneurodegeneration and amyloid accumulation, respectively. The data fromthe additional tests may be uploaded into the cloud of the socialnetwork and combined with or not combined with the previous datacollected for the patient. Data analytics may be performed on thecombined data, such as automated quantitative MRI analysis, or acentralized quality-controlled expert read of a scan. The combined dataand/or results from the analysis may be curated in a concise, summarizedreport as described earlier. In some embodiments, the report may be theintegrated baseline diagnostic report. The integrated baselinediagnostic report may be shared with another physician in the socialnetwork, for instance, the patient's primary care physician. In someembodiments, the specialist may further consult on the integratedreport's data with a data interpretation expert such as in a callcenter, via voice, video, or other messaging within the social network.

FIG. 12 is an example data flow of another example method 1200 ofgenerating a report, arranged in accordance with at least someembodiments described herein. For example, in the method 1200, anintegrated “companion diagnostics” report may be generated from a subsetof a diagnostic evaluation of a patient. The diagnostic evaluation, forexample, may include a quantitation of hippocampal volume based on Mill.The diagnostic evaluation may also include other biomarkers, such as acerebrospinal fluid (CSF) assay. The results from the diagnosticevaluation may be captured and inserted into a cloud of a socialnetwork. The diagnostic evaluation, once in the cloud, may be analyzed.The analytics, such a stratification based on machine learning orstatistical algorithms described earlier, may applied to the diagnosticinformation from the patient and summarized into actionable personalizedtreatment information in the form of an integrated companion diagnosticsreport for a prescriber of a drug. The drug may include adisease-modifying drug for treatment of early AD, for example, in theprodromal disease stage. The integrated companion diagnostics report maypresent the values of the individual components of the diagnostics, plusa calculated combination score, together with label information of anapproved personalized drug intended to be used with the combinationbiomarker as “companion diagnostics.” The drug may be directly orderedfrom a device such as a tablet or wearable computer that is presentingthe integrated companion diagnostics report. The integrated companiondiagnostics report may further be shared with other physicians in thesocial network, such as a specialist.

FIG. 13 is an example data flow of another example method 1300 ofgenerating a report for presentation, arranged in accordance with atleast some embodiments described herein. In some embodiments, the method1300 may begin with a specialist having received a report indicating aneed for integrated companion diagnostics that include actionablepersonalized treatment information for a patient. In some embodiments,the report received by the specialist may be similar to the reportreceived by the specialist in the method 1200.

In particular, the method 1300 may be configured to generate anintegrated longitudinal report for safety and efficacy monitoring. Thereport may be presented to a prescriber of a disease-modifying AD drugtherapy or other AD therapy. Longitudinal monitoring over a period mayinclude following a prescribed treatment for a patient to determine thebenefit of the treatment to the patient and to determine whether thetreatment is having any adverse effects on the patient, such asmicrohemorrhages or vasogenic edema (called ARIA-H or ARIA-E).

The longitudinal monitoring may include a specialist performing and/orordering additional tests of the patient over the period. The tests mayinclude CSF assays or PET scans for efficacy monitoring, such asmeasuring the level of amyloid beta in the CSF or amyloid load in thebrain. Data analytics may be performed on the data, such as automatedquantitative image analysis or a centralized quality-controlled expertread of a scan such as safety reads of MRIs, for presence of ARIA-H orARIA-E. Based on the data analytics, alerts may then be triggered upondetection of potential safety concerns of the drug treatment. The alertsmay trigger messaging within the social network to indicate to thespecialist and other participants in the social network of the alertbeing triggered.

The longitudinal profile from the patient may be also curated in aconcise, summarized form, as described earlier, into the integratedlongitudinal efficacy/safety profile report for therapy monitoring. Insome embodiments, the integrated longitudinal efficacy/safety profilereport may be shared among participants within the social network, suchas a specialist, a personal care provider of the patient, and/or thesubject paying the medical bills of the patient, such as an insurancecompany. In some embodiments, the subject paying the medical bills ofthe patient may make payment on aspects of the treatment of the patientbased on the success of the treatment being within an adequaterisk/benefit ratio. The subject paying the medical bills of the patientprescriber may further consult on the integrated efficacy/safety profiledata with another physician expert, for example, in a call center, viavoice, video, or other messaging within the social network.

In some embodiments, the physician that prescribes the treatment for thepatient may switch the treatment based on how the patient is respondingto the treatment. In some embodiments, the subject paying the medicalbills of the patient may mandate a change in treatment based on reviewof data.

The data analyzed for generating the integrated longitudinalefficacy/safety profile report may include other information about thepatient. For example, the data analyzed may include the patient'sindividual characteristics, including next-generation genome sequencinginformation. Other patient characteristics collected from continuouswireless sensing devices may be incorporated to capture novelbiomarkers, such as activity in multiple sclerosis (using mobileaccelerometers) or micro-invasive, mobile blood sampling/analysis incancer therapy. In some embodiments, therapy monitoring may includetherapy monitoring of a personalized drug therapy, such as antibodytherapy, which may be applied in multiple sclerosis and other diseasessuch as cancer.

In some embodiments, the data from the patient may be anonymized andaggregated with anonymized data from other patients. The anonymizedaggregated data may further be utilized by pharmaceutical companies forgenerating peri- or post-approval data and demonstrating real-worldevidence of a favorable risk/benefit ratio, for instance, forreimbursement purposes.

FIG. 14 is an example data flow of another method 1400 of generating areport for presentation, arranged in accordance with at least someembodiments described herein. The method 1400 may be configured togenerate a predictive analytics report based on analytics of individualbiomarkers, or integration of a multitude of biomarkers of a patient.The predictive analytics report may be generated for a physician, asubject paying the medical bills of the patient, or the patient, priorto the patient receiving a disease-modifying AD drug therapy or other ADtherapy. The predictive report may predict individual response to aparticular prevention strategy, such as prescribing a biologics drug inthe pre-symptomatic, earliest phase of AD. The predictive report mayfurther identify at-risk individuals such as seniors, for instance, totheir family members. The predictive report may further stratifypatients into a particular stage of the AD disease continuum, such asthe pre-symptomatic phase, or earlier, and guide treatments that may beapproved for these phases of disease.

To generate predictive analytics report, the method 1400 may include afirst physician, such as a specialist, ordering a set of predictivebiomarker tests for the patient. The predictive biomarker tests mayinclude an Mill scan, a PET scan, a structural Mill analysis, a DTI Milltractography, a brain connectivity map analysis, a voxel-based amyloidPET analysis, or other advanced brain imaging test. The physician mayfurther order lab-based test such as IVDs or whole genome sequencing,among others. In some embodiments, the whole genome sequencing may beperformed on semiconductor-based nanopore sequencing equipment. The datacollected from the lab-based tests and the predictive biomarker testsmay be captured and combined together in a cloud in the social network,for example, the social network 100 of FIG. 1.

Automated data analytics may be performed on the combined data, asdescribed earlier. The automated data analytics may be performed in ananalytic data center in a building or office of the physician, in aphysical analytic data center, in a physical supercomputer facility, ina cloud-based on-demand compute resource such as Amazon EC2, or in adedicated analytic data center in the cloud. In these and otherembodiments, the analytic data center may be communicatively coupled tothe social network to receive the combined data from the social network.The analytic data center may further utilize quantum computers, nodeswith integrated quantum chips, or nodes optimized for in-memoryanalytics such as tera- or petabyte-order memory.

In some embodiments, the advanced analytics may include real time,automated quantitative analysis of hippocampal volumes or other brainstructures, fiber tract network analysis based on DTI MM, or brainconnectivity map analysis based on ultra-high resolution resting statefMRI, among other analytics. The advanced analytics may further includeNGS genome analysis, analysis of mass spectrometry data, and/or analysisof a combination of biomarkers.

Once the analytics are performed, the physician, such as a specialist,may use a tablet or wearable computer device with a natural userinterface (NUI) to access the advanced analytics capabilities residingin the analytic data center or compute resource that is connected to thesocial network. In some embodiments, the physician may navigate the NUIto identify a set of useful additional predictive biomarker tests to beperformed, based on information already available for the patient anddata residing in the aggregated data repository. In some embodiments,the NUI may invoke advanced analytics to be run against the datarepository, for example, to identify such useful additional markers.After identifying the additional markers, the NUI may then present theresult back to the physician or other participant in the social network.In some embodiments, the advanced analytics report may include datapresented in a quantitative format such as probability, likelihood,and/or score, alongside contextual information from the literatureexplaining the predictive analytics result. In these and otherembodiments, the physician may directly order these predictive testsfrom the social network so they can be performed on data from thepatient and summarized in the predictive analytics report.

FIGS. 10-14 illustrate particular methods for generating reports relatedto Alzheimer's disease. In general, the methods and systems describedherein may be applicable to any personalized health care application andtherapy area, for example, other neurodegenerative diseases, multiplesclerosis, and cancer. The methods and systems described may also beimplemented for application in the diagnosis of post-traumatic stressdisorder (PTSD) or traumatic brain injury (TBI). PTSD and TBI may sharecommon elements in diagnostic approach and have been hypothesized asbeing a contributor to the development of subsequent AD dementia. Otherembodiments may incorporate other imaging modalities, such as FDG-PET,molecular imaging such as nano-particle based MRI, DTI MRI, ASL MRI, andso on. Other non-imaging biomarkers, for example, via continuouswireless monitoring and/or “self tracking” consumer devices, such as bygait sensors, eye tracking, wireless sleep monitors such asaccelerometers and/or EEG, etc., may be implemented using ContinuaAlliance/ISO/IEEE 11073 Personal Health Data (PHD) standards.

FIG. 15 is a flow chart of an example method 1500 of deliveringinformation-enabled personalized healthcare in a clinical, non-researchsetting, arranged in accordance with at least some embodiments describedherein. The method 1500 may be implemented, in some embodiments, by asystem, such as the system 800 of FIG. 8. Although illustrated asdiscrete blocks, various blocks may be divided into additional blocks,combined into fewer blocks, or eliminated, depending on the desiredimplementation.

The method 1500 may begin at block 1502, where one or more data streamsmay be captured. Each of the data streams may be related to health careof a patient. In some embodiments, one of the data streams may becaptured from an application being run on a mobile device related toscreening for early Alzheimer's disease. In some embodiments, one of thedata streams may be captured from a mobile cognitive testing applicationbeing run on a mobile device with respect to the patient. In these andother embodiments, when the mobile cognitive testing applicationindicates a positive diagnostic, another of the data streams is capturedfrom a genetic test ordered for the patient from within the mobilecognitive testing application. In some embodiments, the data streams maycome from other patient test results. In some embodiments, the testresults may be test results from the same type of tests where the testsare performed separately over-time or longitudinally. In someembodiments, the test results may come from a patient, a lab, aspecialist, or a primary care physician. In some embodiments, the datastream may include images or written text, among other types of data.

In block 1504, the data streams may be integrated to generate integrateddiagnostic data. In some embodiments, the data streams may be integratedin a cloud environment. In these and other embodiments, the data streamsmay be associated based on a patient from which the data streamsoriginated. In some embodiments, the data streams may be accessedthrough a social network.

In block 1506, the integrated diagnostic data may be analyzed togenerate analyzed diagnostic data. The analysis of the integrateddiagnostic data may be performed, in some embodiments, by comparing theintegrated diagnostic data with integrated diagnostic data of one ormore other patients that are distinct from the patient or other data.

In block 1508, the analyzed diagnostic data may be curated. Curating theanalyzed diagnostic data may include presenting one or severalbiomarkers alongside contextual information such as medical guidelinesand/or relevant excerpts from the medical literature or links to theoriginal references. Biomarkers may further be presented alongsidenormative and/or age-related ranges, plots of the patient's individualvalue in relation to the normative and/or age related ranges, andmedical images of the patient or representative illustrative othercases.

In block 1510, an integrated report for presentation to a physician ofthe patient may be generated based on the curated analyzed diagnosticdata. In some embodiments, the report may provide information regardingdrug therapy for the patient based on the analyzed diagnostic data.

In some embodiments, the method 1500 may be performed in a cloud-baseddigital health platform for personalized health care with respect toAlzheimer's disease. In some embodiments, the health care diagnostics ofthe patient may relate to a baseline diagnosis of Alzheimer's diseasesuch that the integrated report is a baseline integrated report. In someembodiments, the health care diagnostics of the patient may relate to alongitudinal monitoring of Alzheimer's disease within the patient suchthat the integrated report is an integrated longitudinal safety/efficacymonitoring report.

In some embodiments, the health care diagnostics of the patient mayrelate to therapy monitoring of disease-modifying multiple sclerosistherapeutics such that the integrated report is an integratedlongitudinal safety/efficacy monitoring report.

In some embodiments, the health care diagnostics of the patient mayrelate to biomarkers of the patient, including gene sequencing data andthe analyzing of the integrated diagnostic data includes predictiveanalytics such that the report is a predictive analytics report. Inthese and other embodiments, the predictive analytics may predictAlzheimer's disease at a pre-symptomatic stage. Alternately oradditionally, the predictive analytics may predict a response of apatient to a particular therapy.

One skilled in the art will appreciate that, for this and otherprocesses and methods disclosed herein, the functions performed in theprocesses and methods may be implemented in differing order.Furthermore, the outlined steps and operations are only provided asexamples, and some of the steps and operations may be optional, combinedinto fewer steps and operations, or expanded into additional steps andoperations without detracting from the essence of the disclosedembodiments.

The embodiments described herein may include the use of a specialpurpose or general purpose computer including various computer hardwareor software modules, as discussed in greater detail below.

Embodiments described herein may be implemented using computer-readablemedia for carrying or having computer-executable instructions or datastructures stored thereon. Such computer-readable media may be anyavailable media that may be accessed by a general purpose or specialpurpose computer. By way of example, and not limitation, suchcomputer-readable media may comprise tangible computer-readable storagemedia including RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any otherstorage medium which may be used to carry or store desired program codein the form of computer-executable instructions or data structures andwhich may be accessed by a general purpose or special purpose computer.Combinations of the above may also be included within the scope ofcomputer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

As used herein, the term “module,” “sub-system,” or “component” mayrefer to software objects or routines that execute on the computingsystem. The different components, modules, engines, and servicesdescribed herein may be implemented as objects or processes that executeon the computing system (e.g., as separate threads). While the systemand methods described herein are preferably implemented in software,implementations in hardware or a combination of software and hardwareare also possible and contemplated. In this description, a “computingentity” may be any computing system as previously defined herein, or anymodule or combination of modulates running on a computing system.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art, and areto be construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present inventionshave been described in detail, it should be understood that the variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the invention.

What is claimed is:
 1. A system for electronic delivery of informationin personalized health care, the system comprising: one or more serversconfigured to host a healthcare social network particular to providingpersonalized health care for an individual patient, the one or moreservers configured to: accept the individual patient as a new patient inthe healthcare social network, communicate with testing equipmentcoupled with the healthcare social network, the testing equipmentconfigured to provide signal data that are generated by diagnostictesting performed on the individual patient using the testing equipment,and add content data for the individual patient in the healthcare socialnetwork, the content data including the signal data; and a dataanalytics unit networked to the one or more servers and configured to:receive data signals over the healthcare social network concerning thepersonalized health care of the individual patient, the received datasignals at least in part derived from the content data of the individualpatient shared over the healthcare social network, analyze the datasignals to determine a diagnostic profile for the individual patient,generate a presentation of the diagnostic profile with contextualmedical information, flag potential calculation errors due to hardwarein the data analytics unit by running random check calculations onexternal reference datasets and comparing the random check calculationsagainst a respective reference value, and correct numericalinstabilities of analytics calculations performed by the data analyticsunit by performing a recalculation at a later time or on differenthardware based on the random check calculations.
 2. The system of claim1, wherein the healthcare social network is further configured toprovide biometric identity verification for participants in thehealthcare social network, wherein the biometric identity verificationmay include one or more biometric factors.
 3. The system of claim 1,wherein the diagnostic testing is one or more of: nano-particle basedmagnetic resonance imaging (MM), diffusion tensor imaging (DTI) MRI,arterial spin labeling (ASL) MRI.
 4. The system of claim 1, wherein thedata analytics unit is further configured to perform one or more of a:voxel-based image quantitation, texture analysis of magnetic resonanceimaging (MRI) scans, or brain connectome analysis based on diffusiontensor imaging (DTI) fiber tracking.
 5. The system of claim 1, whereinthe diagnostic profile provides information regarding a diagnosis ofAlzheimer's disease or early Alzheimer's disease.
 6. The system of claim1, wherein the system enables primary care physicians to deliverpersonalized health care in early Alzheimer's disease.
 7. The system ofclaim 1, further comprising a reporting unit networked to the one ormore servers and configured to generate an integrated report based onthe presentation of the diagnostic profile, wherein the integratedreport allows longitudinal therapy monitoring by quantitativelymonitoring one or more of amyloid-related imaging abnormalities (ARIA)types, ARIA-H microhemorrhages, or ARIA-E vasogenic edemas.
 8. Thesystem of claim 1, further comprising a reporting unit networked to theone or more servers and configured to generate an integrated reportbased on the presentation of the diagnostic profile, wherein theintegrated report provides information regarding a diagnosis ofpost-traumatic stress disorder (PTSD) or traumatic brain injury (TBI).9. The system of claim 1, further comprising a reporting unit networkedto the one or more servers and configured to generate an integratedreport based on the presentation of the diagnostic profile, wherein thepersonalized health care of the individual patient relates to therapymonitoring of disease-modifying multiple sclerosis therapeutics suchthat the integrated report is an integrated longitudinal safety/efficacymonitoring report.
 10. The system of claim 1, further comprising areporting unit networked to the one or more servers and configured togenerate an integrated report based on the presentation of thediagnostic profile, wherein the integrated report provides informationregarding an early diagnosis of cancer.
 11. The system of claim 1,wherein the diagnostic testing is from one or more of: micro-invasivesampling of cancer therapy and mobile blood sampling of cancer therapy.12. The system of claim 1, further comprising a reporting unit networkedto the one or more servers and configured to generate an integratedreport based on the presentation of the diagnostic profile, wherein theintegrated report is presented by an interactive dashboard applicationparticular to personalized health care that is presented by one or moreof the following: a flexible display device folded into drug packagingmaterials, a disposable display device folded into drug packagingmaterials, a projector device networked to the one or more servers andconfigured to wirelessly communicate with a wearable computer wristbandand, based on the communication, present the interactive dashboardapplication with one or more of the content data for the individualpatient and the integrated report of the individual patient beingprovided in the interactive dashboard application, and an image viewingdevice configured to present actual medical images of the individualpatient in 2-D, 3-D, or 4-D, wherein the image viewing device includesadvanced visualization features including plots of quantitative valuesoverlaid with their respective source images when data points on amedical image are selected.
 13. A computer-implemented method ofdelivering information-enabled personalized health care with respect toearly Alzheimer's disease in a clinical, non-research setting, thecomputer-implemented method comprising: provisioning, using one or moreservers, a healthcare social network particular to providingpersonalized health care for an individual patient, the healthcaresocial network configured to: accept the individual patient as a newpatient in the healthcare social network, add content data for theindividual patient, the content data including one or more physicalmeasurements from diagnostic testing performed on the individual patientusing testing equipment coupled with the healthcare social network,specify recipients within the healthcare social network particular tothe individual patient for delivery of the content data, and provide asecure interface for multiple human participants particular to theindividual patient to exchange bidirectional secure correspondencewithin the healthcare social network regarding the personalized healthcare of the individual patient and transfer the content data therebetween based on permissions within the healthcare social network,capturing one or more data streams, each of the data streams related tohealth care of the individual patient provided by one or more of themultiple human participants, including the correspondence between themultiple human participants, wherein one or more of the data streamsinclude the content data that includes results from the diagnostictesting of the individual patient; analyzing, using one or moreprocessors, the one or more data streams to generate a diagnosticprofile for the individual patient; flagging potential calculationerrors, due to hardware in the one or more processors analyzing the oneor more data streams, by running random check calculations on externalreference datasets and comparing the random check calculations against arespective reference value; correct numerical instabilities of analyticscalculations performed by the one or more processors analyzing the oneor more data streams by performing a recalculation at a later time or ondifferent hardware based on the random check calculations; curating,using the one or more processors, the diagnostic profile by presentingthe diagnostic profile with contextual medical information; andgenerating, using the one or more processors, an integrated report forpresentation to a physician of the individual patient using the curateddiagnostic profile, the integrated report for providing a personalizedtherapy for the individual patient with respect to the curateddiagnostic profile.
 14. The computer-implemented method of claim 13,wherein one of the data streams is captured from an application beingrun on a mobile or wearable device related to screening for earlyAlzheimer's disease.
 15. The computer-implemented method of claim 13,wherein one of the data streams is captured from one or more of: agenetic test for an apolipoprotein E (APOE) genotype, testing forcertain genetic variations including single-nucleotide polymorphisms(SNPs), a blood screening test for Alzheimer's disease, an eye-basedtest configured to detect a presence of ocular deposits of amyloid betapeptide, a gait sensor device, an eye tracking device, a wireless sleepmonitor, an EEG device, or a cerebrospinal fluid (CSF) assay for tauprotein or amyloid beta peptide of the individual patient derived from aspinal tap procedure performed on the individual patient.
 16. Thecomputer-implemented method of claim 13, wherein the personalized healthcare of the individual patient relates to a baseline diagnosis ofAlzheimer's disease with respect to the individual patient such that theintegrated report is a baseline integrated report for the individualpatient.
 17. The computer-implemented method of claim 13, wherein thepersonalized health care of the individual patient relates to alongitudinal monitoring of Alzheimer's disease within the individualpatient such that the integrated report is an integrated longitudinalsafety/efficacy monitoring report.
 18. The computer-implemented methodof claim 13, wherein the personalized health care of the individualpatient relates to biomarkers of the individual patient, including genesequencing data, and analyzing the one or more data streams includespredictive analytics such that the integrated report is a predictiveanalytics report.
 19. The computer-implemented method of claim 18,wherein the predictive analytics predicts Alzheimer's disease at apre-symptomatic stage.
 20. The computer-implemented method of claim 18,wherein the predictive analytics predicts a response of the individualpatient to a particular therapy.
 21. A computing system for advanced,automated analytics of data on a personalized health care socialnetwork, the system comprising: one or more servers configured to host ahealthcare social network particular to providing personalized healthcare for an individual patient, the healthcare social network configuredto: accept the individual patient as a new patient in the healthcaresocial network, add content data for the individual patient, specifyrecipients within the healthcare social network particular to theindividual patient for delivery of the content data, and provide asecure interface for multiple human participants particular to theindividual patient to exchange bidirectional secure correspondencewithin the healthcare social network regarding the personalized healthcare of the individual patient and transfer the content data therebetween, the correspondence including communications regarding aparticular treatment for the individual patient; a data anonymizer unitnetworked to the one or more servers and configured to receive thecontent data associated with the personalized health care of theindividual patient through the healthcare social network and to removeat least a portion of patient identifying information from the receivedcontent data to generate first anonymized data; a data analytics unitnetworked to the data anonymizer unit and configured to: analyze thefirst anonymized data to generate analytic information with respect tothe individual patient, the analysis of the first anonymized dataperformed based on the analytic information including at least onediagnostic marker for the individual patient, flag potential calculationerrors due to hardware in the data analytics unit by running randomcheck calculations on external reference datasets and comparing therandom check calculations against a respective reference value, andcorrect numerical instabilities of analytics calculations performed bythe data analytics unit by performing a recalculation at a later time oron different hardware based on the random check calculations; and anaugmented reality (AR) user interface device networked to the dataanalytics unit and configured to provide computer instructions forpresentation to a AR user interface that is configured to presentinformation including the analytic information generated by the dataanalytics unit.
 22. The system of claim 21, wherein the data analyticsunit is configured to analyze the first anonymized data based on amachine learning data analytics operation.
 23. The system of claim 22,wherein when the first anonymized data is biomarker combination data,the data analytics unit is further configured to predict one or more oftreatment response and future onset of disease, in a pre-symptomaticAlzheimer's disease patient based on a biomarker combination data. 24.The system of claim 21, wherein the data analytics unit is configured toanalyze the first anonymized data using a quantum computer, or a computenode with integrated quantum chips.